The core of this course revolves around programming machine learning algorithms for yourself, as a way to truly understand what exactly they are learning. Each of the different modules will focus on programming a different algorithm, understanding the math required for that algorithm, and discussing a philosophical question or a societal impact related to applying this algorithm in practice.
Specifically, we’ll cover the following algorithms: k-Nearest Neighbours; Naive Bayes; Gradient Descent; (Multivariate) Linear Regression; Polynomial Regression; k-Means
Note: This explicitly does not include Neural Networks, as that is too large a topic to also include here. However, the foundational concepts covered are also all applied within Neural Networks, and so this does provide the necessary basis to study Neural Networks in a follow-up course (IML2).